1. Fields of the Invention
The present invention relates generally to surveillance systems and more particularly to an image sensor, a motion image sensor, and improved, cost-effective image analysis and motion image analysis methods.
2. Description of the Prior Art
Generally, in many imaging systems implemented for detecting moving objects in an image field, there is a cost associated with sampling parts of the image. This cost includes but is not limited to:                a. A cost in energy in irradiating the subject, e.g. electricity bill for lighting;        b. A cost in damaging the subject, for instance, high exposure to X-rays with fluoroscopy;        c. A cost in being detected, which might apply in certain security applications, where it is desired to want someone not to know if they are being observed;        d. A cost in sensor time where the sensor has to be physically moved, and hence time is wasted by sampling uninteresting areas; and,        e. A cost in sensor time where an image sensor device requires time to reset between sampling.        
In any moving image, there are typically areas of interest and areas that are not of interest. Traditional imaging systems expend the same amount of cost to sample all areas of the image. When an image comes to be stored on a non-volatile medium, the moving image will typically be compressed. Compression reduces the amount of data to be stored, but it does not remove the cost of making the image in the first place.
Jonas Nilsson in the work entitled “Visual Landmark Selection and Recognition for Autonomous Unmanned Aerial Vehicle Navigation”—Master's degree project, The Royal Institute of Technology, Sweden, 2005, (hereinafter Nilsson)—disclosed image analysis methods that aim at enhancing the performance of a navigation system for an autonomous unmanned aerial vehicle. Nilsson investigates algorithms for the selection, tracking and recognition of landmarks based on the use of local scale invariant features. For example, Nilsson disclosed the affine tracker algorithm combined with Kalman filters to track an object in an image plane. Nilsson further discloses a landmark recognition algorithm that has the capability of recognizing landmarks despite the presence of noise and significant variation in scale and rotation. The performance of the landmark recognition algorithm allows for a substantially reduced sampling rate but the uncertainty of the landmark location results in larger image search areas and therefore an increase in the computational burden. That means the landmark recognition algorithm is more suitable for stable (i.e. unchanging) image regions than for unstable (i.e. changing) image regions.
Therefore, it would be highly desirable to provide a system and method for minimizing the cost in monitoring a visual space (i.e. everything that a camera sees).